Rebecca Parsons, former CTO emerita of ThoughtWorks and co-author of 'Building Evolutionary Architectures', joins Neal Ford, a regular host and also co-author, to dive into the dynamic world of AI governance. They explore how fitness functions can optimize AI performance, ensuring systems meet their intended goals. The duo discusses identifying biases within AI, the importance of operationalizing large language models, and the need for objective metrics in rapidly changing tech landscapes. Their insights reveal how adaptability can shape the future of AI.
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insights INSIGHT
Defining 'Good' with Fitness Functions
Fitness functions measure how well a solution fulfills its goals, like minimizing distance in the traveling salesman problem.
They help balance competing goals, like throughput and variety on a production line, by defining 'good'.
insights INSIGHT
Capabilities vs. Behavior
Fitness functions test a system's capabilities ('ilities'), like scalability or elasticity, not its domain behavior.
This complements traditional testing by focusing on architectural characteristics.
volunteer_activism ADVICE
Guiding AI Evolution
Define the desired capabilities of your AI system, like acceptable latency.
Build fitness functions around these capabilities to guide evolution and enable quick adaptation to new models or vendors.
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This book offers a unique, visually rich approach to learning software architecture. It covers the basics of software architecture, including the distinction between architecture and design, common architectural styles, and tradeoffs involved in architectural decisions. The book is designed to engage readers through a multisensory learning experience, making it easier to understand and retain complex concepts. It is particularly recommended for software developers looking for a quick introduction to software architecture[1][3][4].
Building Evolutionary Architectures
Neal Ford
Patrick Kua
Rebecca Parsons
Pramod Sadalage
Software Architecture: The Hard Parts
Ford, Richards, Sadalage & Dehghani
Architecture Code
Architecture Code
None
Neal Ford
AI is inherently dynamic: that's true in terms of the field itself, and at a much lower level too — models are trained on new data and algorithms adapt and change to new circumstances and information. That's part of its power and what makes it so exciting, but from a business and organizational perspective, that can make governance and measurement exceptionally difficult. How can we know that our AI is optimized for the right thing? How can we be sure it's oriented towards what we want it to be?
This is where the concept of fitness functions can help. Broadly speaking, fitness functions are ways of measuring the extent to which a given solution is fulfilling its goals — so, in the context of AI, they can help teams ensure that AI systems are serving their intended purpose.
In this episode of the Technology Podcast, Rebecca Parsons and Neal Ford — authors (alongside Pat Kua and Pramod Sadalage) of Building Evolutionary Architectures, the book which brought fitness functions into the software architecture space — join host Ken Mugrage to explore how the fitness function concept can help us better manage the dynamism of AI and, in doing so, overcome the challenge of bringing such systems into production.